返回到 Probabilistic Graphical Models 1: Representation

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Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....

Jul 13, 2017

Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!

Oct 23, 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

篩選依據：

創建者 Chahat C

•May 04, 2019

lectures not good(i mean not detailed)

創建者 Sumod K M

•May 06, 2019

The course contents and presentation is of very high quality. The assignments and quizzes are both challenging and very rewarding. The only minor qualm is that the programming assignment grader seems to have few issues. For one, MATLAB indexing is really hard to work with. Secondly, it doesn't test the answers fully in some cases. Like the case of OptimizeWithJointUtility, OptimizeLinearExpectations. My codes passed the grader but I was splitting to hair to figure out why my answers to quiz questions corresponding to programming assignment were wrong. Turned out that my code was incorrect for the two programming assignments and that was causing issues. Otherwise, really nice course. Thank you :).

創建者 Yue S

•May 09, 2019

Great course!

創建者 郭玮

•Apr 26, 2019

Really nice course, thank you!

創建者 HOLLY W

•May 25, 2019

课程特别好，资料丰富

創建者 Vivek G

•Apr 27, 2019

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

創建者 Jui-wen L

•Jun 21, 2019

Easy to follow and very informative.

創建者 Nijesh

•Jul 18, 2019

Thanks for offering

創建者 Harshdeep S

•Jul 19, 2019

Excellent blend of maths & intuition.

創建者 Anthony L

•Jul 20, 2019

Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.

創建者 Mike P

•Jul 30, 2019

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

創建者 Parag H S

•Aug 14, 2019

Learn the basic things in probability theory

創建者 Ayush T

•Aug 23, 2019

This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

創建者 Meysam G

•Sep 12, 2019

I had actually read the David Barber book before I took this course. The course provides a deep insight to the PGMs which is necessary if one wants to utilize it in real applications or as in my case in research works. Moreover, the language of the instructor is comfortably plain, especially when it comes to explaining somewhat complicated concepts. In general, it is highly recommended.

創建者 Myoungsu C

•Dec 26, 2018

Writing on the ppt is not clear to see.

創建者 Xiaojie Z

•Dec 22, 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

創建者 Stephen A

•May 18, 2018

I really enjoyed this course. Prof Koller presents the material very well, and it's really interesting to see how probabilistic graphical model frameworks are underpinned mathematically. I thought it was a pretty tough course at points, and while the lectures are good I found having a copy of Prof Koller's textbook very useful.

I would give this course 5 stars, but I thought some of the programming assignments involved too much grappling with MATLAB rather than illuminating the principles in the lectures. Also, I think the order of the lectures may have been changed since the course was first run as there are occasional references to things that have not been covered at that point.

Overall though, very enjoyable. I'm looking forward to parts 2 and 3.

創建者 serge s

•Oct 18, 2016

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

創建者 Akshaya T

•Jan 16, 2018

Some tutorials need disambiguating documentation (upgrade :)) but otherwise, the course is really good. It would also help if there is a mention of what chapters to study from the book for every lesson -- in the slides.

創建者 Jhonatan d S O

•May 25, 2017

Rich content and useful tools for applying in real problems

創建者 Ashwin P

•Jan 09, 2017

Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

創建者 Surender K

•Nov 07, 2016

Wonderful course with great material. Wish there were more examples in the material. Nonetheless cannot complain to get this course for free with SEE material and programming assignments (need to complete yet in this session)

創建者 Nikesh B

•Nov 06, 2016

Excellent

創建者 Dat N

•Mar 28, 2018

The course helps me understand what a probabilistic graphical model is and how and why it works. One aspect I like the most about the course is the programming assignments. Those PA really make a lot of concepts clearer although sometimes you need to think carefully when the instruction is hard to follow. I think there should be more test case and expected results so that students know what is asked and to evaluate their own code. The instructor is generally clear but sometimes she goes too fast on certain concept. The course is hard but if you gives in time and effort you can complete it.

創建者 Sunil

•Sep 12, 2017

Great intro to probabilistic models